WO2023276737A1 - 気流制御システム、及び、気流制御方法 - Google Patents

気流制御システム、及び、気流制御方法 Download PDF

Info

Publication number
WO2023276737A1
WO2023276737A1 PCT/JP2022/024394 JP2022024394W WO2023276737A1 WO 2023276737 A1 WO2023276737 A1 WO 2023276737A1 JP 2022024394 W JP2022024394 W JP 2022024394W WO 2023276737 A1 WO2023276737 A1 WO 2023276737A1
Authority
WO
WIPO (PCT)
Prior art keywords
person
airflow
control system
amount
dust
Prior art date
Application number
PCT/JP2022/024394
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
和雄 伊藤
達雄 古賀
千人 浦
Original Assignee
パナソニックＩｐマネジメント株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by パナソニックＩｐマネジメント株式会社 filed Critical パナソニックＩｐマネジメント株式会社
Priority to JP2023531806A priority Critical patent/JP7702676B2/ja
Publication of WO2023276737A1 publication Critical patent/WO2023276737A1/ja

Links

Images

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/32—Responding to malfunctions or emergencies
    • F24F11/36—Responding to malfunctions or emergencies to leakage of heat-exchange fluid
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
    • F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
    • F24F11/79—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling the direction of the supplied air
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F9/00—Use of air currents for screening, e.g. air curtains
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion

Definitions

  • the present invention relates to an airflow control system and an airflow control method.
  • Patent Literature 1 discloses an air conditioner that automatically selects a portion to which air is applied according to the elapsed time and controls air blowing without the user changing the operation mode.
  • the present invention provides an airflow control system and an airflow control method that can control the airflow based on the estimation result of the dust generation amount.
  • An airflow control system includes an acquisition unit that acquires image data of an image of a person; an identification unit that identifies features related to the appearance of the person based on the obtained image data; an estimating unit for estimating an amount of dust generated from the person based on the obtained features related to the appearance of the person; and an air flow for reducing the dust based on the estimated amount of dust generated. and a control unit that controls the airflow generation device.
  • An airflow control method includes an acquisition step of acquiring image data of an image in which a person is captured; a specifying step portion of specifying features related to the appearance of the person based on the acquired image data; an estimation step of estimating an amount of dust generated from the person based on the specified features related to the appearance of the person; and generating an airflow for reducing the dust based on the estimated amount of dust generated. and a control step of controlling the airflow generator.
  • a program according to one aspect of the present invention is a program for causing a computer to execute the airflow control method.
  • the airflow control system and airflow control method of the present invention can control the airflow based on the result of estimating the amount of dust generated.
  • FIG. 1 is a block diagram showing a functional configuration of an airflow control system according to Embodiment 1.
  • FIG. FIG. 2 is a diagram showing a schematic configuration of an airflow generation device included in the airflow control system according to Embodiment 1.
  • FIG. FIG. 3 is a flowchart of operation example 1 of the airflow control system according to the first embodiment.
  • FIG. 4 is a conceptual diagram of a machine learning model for identifying the texture of clothing.
  • FIG. 5 is a diagram showing an example of table information for estimating the amount of dust generated from the texture of clothes.
  • 6 is a flowchart of an operation example 2 of the airflow control system according to the first embodiment.
  • FIG. FIG. 7 is a conceptual diagram of a machine learning model for identifying a person's hairstyle.
  • FIG. 8 is a diagram showing an example of table information for estimating the amount of dust generated from a person's hairstyle.
  • FIG. 9 is a block diagram showing a functional configuration of an airflow control system according to Embodiment 2.
  • FIG. 10 is a diagram showing a schematic configuration of an airflow generation device included in the airflow control system according to the second embodiment.
  • FIG. 11 is a flowchart of operation example 1 of the airflow control system according to the second embodiment.
  • FIG. 12 is a flowchart of operation example 2 of the airflow control system according to the second embodiment.
  • FIG. 13 is a flowchart of operation example 3 of the airflow control system according to the second embodiment.
  • FIG. 14 is a diagram showing an example of a skeleton model.
  • each figure is a schematic diagram and is not necessarily strictly illustrated. Moreover, in each figure, the same code
  • FIG. 1 is a block diagram showing a functional configuration of an airflow control system according to Embodiment 1.
  • FIG. 2 is a diagram showing a schematic configuration of an airflow generation device included in the airflow control system according to Embodiment 1.
  • FIG. 1 is a block diagram showing a functional configuration of an airflow control system according to Embodiment 1.
  • FIG. 2 is a diagram showing a schematic configuration of an airflow generation device included in the airflow control system according to Embodiment 1.
  • FIG. 1 is a block diagram showing a functional configuration of an airflow control system according to Embodiment 1.
  • FIG. 2 is a diagram showing a schematic configuration of an airflow generation device included in the airflow control system according to Embodiment 1.
  • the airflow control system 10 for example, an air shower in a subspace 50 (front chamber) provided at the entrance of a main space such as an office space (such as a main space 60 to be described later). It is a system that controls the airflow as A camera 20 and an airflow generator 40 are provided in the subspace 50 .
  • the airflow control system 10 is a system that acquires image data of an image of a person output by the camera 20 and controls an airflow (air shower) generated by the airflow generation device 40 based on the acquired image data.
  • the airflow control system 10 includes a camera 20 , a control device 30 and an airflow generation device 40 .
  • the camera 20 is installed, for example, on the ceiling or wall of the subspace 50, and captures an image (moving image composed of a plurality of images) including a person located in the subspace 50 as a subject.
  • the camera 20 also transmits image data of the captured image to the control device 30 .
  • the camera 20 may be a camera using a CMOS (Complementary Metal Oxide Semiconductor) image sensor, or may be a camera using a CCD (Charge Coupled Device) image sensor.
  • CMOS Complementary Metal Oxide Semiconductor
  • CCD Charge Coupled Device
  • the camera 20 may be a camera using an image sensor capable of detecting infrared rays (infrared light). That is, camera 20 may be an infrared camera. This allows the camera 20 to capture an image (infrared image) even when the subspace 50 is dark.
  • the airflow control system 10 may include two or more cameras 20 .
  • the control device 30 receives image data from the camera 20 and controls the airflow generation device 40 based on the received image data.
  • the control device 30 is, for example, a local controller (i.e., edge computer, etc.) installed in the same facility as the facility in which the subspace 50 is provided, but a server device (i.e., cloud computer, etc.) installed outside the facility. ).
  • the control device 30 includes a communication section 31 , an information processing section 32 and a storage section 33 .
  • the communication unit 31 is a communication module (communication circuit) for the control device 30 to communicate with the camera 20 and the airflow generation device 40.
  • the communication unit 31 for example, receives image data from the camera 20 and transmits control signals to the airflow generation device 40 .
  • the communication performed by the communication unit 31 may be wireless communication or wired communication.
  • the communication standard used for communication is also not particularly limited.
  • the information processing section 32 acquires the image data of the image received by the communication section 31, and performs information processing for controlling the airflow generation device 40 based on the acquired image data.
  • the information processing section 32 is specifically realized by a processor or a microcomputer.
  • the information processing section 32 includes an acquisition section 34 , an identification section 35 , an estimation section 36 and a control section 37 .
  • the functions of the acquisition unit 34, the identification unit 35, the estimation unit 36, and the control unit 37 are realized by the processor or microcomputer constituting the information processing unit 32 executing the computer program stored in the storage unit 33. . Details of the functions of the acquisition unit 34, the identification unit 35, the estimation unit 36, and the control unit 37 will be described later.
  • the storage unit 33 is a storage device that stores image data received by the communication unit 31, computer programs executed by the information processing unit 32, and the like.
  • the storage unit 33 also stores a machine learning model, an estimation model, and the like, which will be described later.
  • the storage unit 33 is implemented by a semiconductor memory, HDD (Hard Disk Drive), or the like.
  • the airflow generation device 40 is installed in the subspace 50 and forms an airflow in the subspace 50 .
  • the airflow generation device 40 includes a blower fan 41 .
  • the blower fan 41 generates airflow in the subspace 50 by rotating.
  • the airflow generation device 40 may include a plurality of blower fans 41 .
  • the amount of air blown by the blower fan 41 that is, the strength of the airflow
  • the period during which the air is blown are changed based on the control signal transmitted from the control device 30 .
  • FIG. 3 is a flow chart of Operation Example 1 of the airflow control system 10 .
  • the camera 20 takes an image of the person and transmits the image data of the image to the control device 30 .
  • the communication unit 31 of the control device 30 receives the image data of the image of the subspace 50 from the camera 20 (S11), and the information processing unit 32 stores the received image data in the storage unit 33 (S12).
  • the acquisition unit 34 acquires the image data received by the communication unit 31 and stored in the storage unit 33 (S13).
  • the specifying unit 35 specifies the texture of the clothes of the person in the image based on the acquired image data (S14).
  • the identifying unit 35 identifies the texture of the clothes using, for example, a machine learning model.
  • FIG. 4 is a conceptual diagram of such a machine learning model.
  • the machine learning model for identifying the texture of clothing is configured to be able to identify the texture of clothing using a large number of images of a person wearing clothing as learning data. It is a machine learning model that has been processed and is stored in the storage unit 33 in advance.
  • the images used as learning data are labeled with identification information (such as texture A) of the texture of the clothes reflected in the images.
  • identification information such as texture A
  • the texture of clothing can be rephrased as the surface condition of clothing.
  • the machine learning model outputs a classification score based on machine learning such as a convolutional neural network (CNN).
  • the classification score is, for example, texture A (smooth): 0.60, texture B (stiff): 0.20, and so on. It is a score that indicates whether the quality is high.
  • the identifying unit 35 identifies the texture with the highest classification score as the texture of the clothing of the person in the image.
  • the estimation unit 36 estimates the amount of dust generated based on the specified texture of the clothing (S15). For example, the estimation unit 36 estimates the dust generation amount by referring to table information that associates the texture of clothing with the amount of dust expected to be generated from clothing having the texture.
  • FIG. 5 is a diagram showing an example of such table information. Such table information is stored in the storage unit 33 in advance. The amount of generated dust shown in the table information shown in FIG. 5 is appropriately determined empirically or experimentally, for example, by a designer of the airflow control system 10 or the like.
  • the control unit 37 controls the airflow generation device 40 based on the estimated dust generation amount (S16).
  • the airflow generation device 40 is controlled by transmitting a control signal from the communication unit 31 to the airflow generation device 40 .
  • the control unit 37 increases the amount of air blown by the blower fan 41 as the amount of dust generated increases (the amount of air blown by the blower fan 41 increases). control the airflow generator 40 so that it becomes stronger).
  • the control unit 37 controls the airflow generation device so that the generation time of the airflow increases as the amount of dust generated increases. control 40;
  • the airflow control system 10 identifies the texture of a person's clothing based on image data.
  • the airflow control system 10 is an airflow generation device that estimates the amount of dust generated from a person based on the texture of the specified clothing of the person, and generates an airflow toward the person based on the estimated amount of dust generated. control 40; As a result, the airflow control system 10 can effectively reduce dust expected to be generated from the clothing of the person (reduce dust around the person).
  • the airflow control system 10 may specify the amount of clothing based on the image data.
  • the amount of clothes includes not only the number of clothes, but also the amount of cloth according to the type of clothes (thick cloth, thin cloth, sleeves, hem, length, etc.). That is, the airflow control system 10 estimates the amount of dust generated from the person based on the specified amount of clothing of the person, and based on the estimated amount of dust generated, generates an airflow toward the person.
  • the generating device 40 may be controlled.
  • the identifying unit 35 identifies the amount of clothing using, for example, a machine learning model.
  • the machine learning model for identifying the amount of clothing is a machine learning model configured to be able to identify the amount of clothing using a large number of images of a person wearing clothes as learning data, It is stored in the storage unit 33 in advance.
  • the images used as learning data are labeled with identification information (very large, large, normal, small, very small, etc.) indicating the amount of clothing reflected in the image.
  • the machine learning model outputs a classification score based on machine learning such as convolutional neural networks.
  • the classification score is, for example, quite large: 0.60, large: 0.20, etc., indicating how much clothing is shown in the image (how much is likely to be). is.
  • the identifying unit 35 identifies the amount of clothing with the highest classification score as the amount of clothing of the person appearing in the image.
  • the estimating unit 36 estimates the amount of generated dust based on the specified amount of clothing. For example, the estimation unit 36 estimates the amount of generated dust by referring to table information that associates the amount of clothing with the amount of dust expected to be generated from the clothing. Such table information is stored in the storage unit 33 in advance. In the table information, for example, the amount of generated dust is determined so that the amount of generated dust increases as the amount of clothing increases. The dust generation amount indicated in the table information is appropriately determined empirically or experimentally, for example, by a designer of the airflow control system 10 or the like.
  • the airflow control system 10 identifies features related to the person's clothing (at least one of the texture of the person's clothing and the amount of the person's clothing) based on the image data.
  • the airflow control system 10 estimates the amount of dust generated from the person based on the identified characteristics of the person's clothing, and adjusts the airflow (to the person) to reduce the dust based on the estimated amount of dust generated.
  • control an airflow generator 40 that generates a directed airflow.
  • the airflow control system 10 can effectively reduce dust expected to be generated from people's clothes (keep away from people's surroundings).
  • the airflow control system 10 (estimating unit 36) may comprehensively estimate the amount of dust based on both the texture of clothing and the amount of clothing.
  • FIG. 6 is a flow chart of Operation Example 2 of the airflow control system 10 .
  • the camera 20 takes an image of the person and transmits the image data of the image to the control device 30 .
  • the communication unit 31 of the control device 30 receives the image data of the image of the subspace 50 from the camera 20 (S21), and the information processing unit 32 stores the received image data in the storage unit 33 (S22).
  • the acquisition unit 34 acquires the image data received by the communication unit 31 and stored in the storage unit 33 (S23), and the specifying unit 35 determines the image data that appears in the image based on the acquired image data.
  • a person's hairstyle is specified (S24).
  • the identifying unit 35 identifies a person's hairstyle using, for example, a machine learning model.
  • FIG. 7 is a conceptual diagram of such a machine learning model.
  • the machine learning model for identifying a person's hairstyle is a machine learning model configured to be able to identify a person's hairstyle using a large number of images of a person as learning data. Yes, and is stored in the storage unit 33 in advance. Images used as learning data are labeled with identification information of a person's hairstyle (straight short hair, straight long hair, permed short hair, etc.).
  • the machine learning model outputs a classification score based on machine learning such as convolutional neural networks.
  • the classification score is, for example, straight short hair: 0.60, straight long hair: 0.20, and so on.
  • the identifying unit 35 identifies the hairstyle of the person with the highest classification score as the hairstyle of the person in the image.
  • the estimation unit 36 estimates the amount of dust generated based on the specified hairstyle of the person (S25). For example, the estimation unit 36 estimates the amount of dust generated by referring to table information that associates a person's hairstyle with the amount of dust expected to be generated from the person's hair.
  • FIG. 8 is a diagram showing an example of such table information. Such table information is stored in the storage unit 33 in advance.
  • the amount of generated dust shown in the table information shown in FIG. 8 is appropriately determined empirically or experimentally, for example, by a designer of the airflow control system 10 or the like. Note that in the table information, for example, the amount of dust generated is determined such that the more dust tends to accumulate on a person's hairstyle, the more dust is generated.
  • the control unit 37 controls the airflow generation device 40 based on the estimated dust generation amount (S26).
  • the airflow generation device 40 is controlled by transmitting a control signal from the communication unit 31 to the airflow generation device 40 .
  • the control unit 37 increases the amount of air blown by the blower fan 41 as the amount of dust generated increases (the amount of air blown by the blower fan 41 increases). control the airflow generator 40 so that it becomes stronger).
  • the control unit 37 controls the airflow generation device so that the generation time of the airflow increases as the amount of dust generated increases. control 40;
  • the airflow control system 10 identifies a person's hairstyle based on image data.
  • the airflow control system 10 estimates the amount of dust generated from the person based on the specified hairstyle of the person, and controls the airflow generation device 40 to generate an airflow toward the person based on the estimated amount of dust generated. Control.
  • the airflow control system 10 can effectively reduce dust that is expected to be generated from human hair (keep it away from people's surroundings).
  • the airflow control system 10 may specify the amount of human hair based on the image data. That is, the airflow control system 10 estimates the amount of dust generated from the person based on the specified amount of hair of the person, and generates an airflow toward the person based on the estimated amount of dust generated.
  • the generating device 40 may be controlled.
  • the identifying unit 35 identifies the amount of human hair using, for example, a machine learning model.
  • the machine learning model for identifying the amount of human hair is a machine learning model configured to be able to identify the amount of human hair using a large number of images of people as learning data. It is stored in section 33 .
  • the images used as learning data are labeled with identification information (very large, large, normal, small, very small, etc.) indicating the amount of hair of a person shown in the image.
  • the machine learning model outputs a classification score based on machine learning such as convolutional neural networks.
  • the classification score is, for example, quite large: 0.60, large: 0.20 . is the score shown.
  • the identifying unit 35 identifies the amount of hair of the person with the highest classification score as the amount of hair of the person appearing in the image.
  • the estimating unit 36 estimates the amount of generated dust based on the specified amount of human hair. For example, the estimation unit 36 estimates the amount of dust generated by referring to table information that associates the amount of human hair with the amount of dust expected to be generated from the human hair. Such table information is stored in the storage unit 33 in advance. In the table information, for example, the amount of generated dust is determined so that the amount of generated dust increases as the amount of human hair increases. The dust generation amount indicated in the table information is appropriately determined empirically or experimentally, for example, by a designer of the airflow control system 10 or the like.
  • the airflow control system 10 identifies the characteristics of the person's hair (at least one of the person's hairstyle and the amount of the person's hair) based on the image data.
  • the airflow control system 10 estimates the amount of dust generated from the person based on the identified hair-related features of the person, and adjusts the airflow (to the person) to reduce the dust based on the estimated amount of dust generated.
  • control an airflow generator 40 that generates a directed airflow.
  • the airflow control system 10 can effectively reduce dust that is expected to be generated from human hair (keep it away from people's surroundings).
  • the airflow control system 10 may comprehensively estimate the dust amount based on both the person's hairstyle and the amount of the person's hair.
  • FIG. 9 is a block diagram showing a functional configuration of an airflow control system according to Embodiment 2.
  • FIG. 10 is a diagram showing a schematic configuration of an airflow generation device included in the airflow control system according to the second embodiment.
  • the airflow control system 70 is a system that acquires image data of the main space 60 output by the camera 20 and controls the airflow of the main space 60 based on the acquired image data.
  • the main space 60 is, for example, an office space, but it may also be a space in a commercial facility, or an indoor space in other facilities such as a space in a house.
  • the airflow control system 70 includes a camera 20, a control device 30, and an airflow generation device 80. FIG.
  • the camera 20 and the control device 30 are substantially the same devices as in Embodiment 1, except that they are intended for the main space 60 and the airflow generation device 80, so descriptions thereof will be omitted.
  • the airflow generator 80 is installed in the main space 60 and forms an airflow in the main space 60 .
  • the airflow generation device 80 includes a blower fan 81 and a louver 82 .
  • the blower fan 81 generates airflow in the main space 60 by rotating.
  • the airflow generation device 80 may include a plurality of blower fans 81 .
  • the amount of air blown by the blower fan 81 (that is, the strength of the airflow) and the period during which air is blown are changed based on the control signal transmitted from the control device 30 .
  • the louver 82 is a structure for changing the direction of airflow.
  • the louver 82 is, in other words, a guide structure that guides airflow.
  • the louver 82 is, for example, a wing-shaped structure whose attitude (wing angle) is changed based on a control signal transmitted by the control device 30 .
  • the airflow generation device 80 is, for example, a dedicated device for the airflow control system 70, a ventilation device or an air conditioner installed in the main space 60 in advance may be used as the airflow generation device 80.
  • FIG. 11 is a flowchart of operation example 1 of the airflow control system 70 .
  • the specifying unit 35 of the control device 30 performs a process of specifying the amount of clothing of the person in the image based on the image data output by the camera 20 (S31). More specifically, the process of step S31 is as described in the operation example 1 of the first embodiment, and is performed periodically.
  • the estimation unit 36 determines whether or not the clothing has changed (S32). For example, the estimation unit 36 determines whether or not the amount of clothing identified last time is different from the amount of clothing identified this time. If there is no change in the clothes, it is considered that the person is not wearing or removing the clothes and dust is not generated. Therefore, when the estimating unit 36 determines that there is no change in the clothes (No in S32), the estimating unit 36 estimates that the person is not wearing clothes and dust is not generated (the amount of dust generated is zero). (S33). In this case, the process of step S31 is continued periodically.
  • the control unit 37 performs control to strengthen the airflow generated by the airflow generation device 80 (S35). Control of the airflow generation device 80 is performed by transmitting a control signal from the communication unit 31 to the airflow generation device 80 . Specifically, the control unit 37 controls the amount of air blown by the blower fan 81 so that the airflow directed toward the person in the image becomes stronger.
  • the airflow control system 70 can effectively reduce the dust generated around the person when the person puts on and takes off the clothes (keep it away from the person's surroundings).
  • the airflow control system 70 can guide generated dust to a predetermined area of the main space 60 .
  • the control to strengthen the airflow may be control to operate the stopped airflow generation device 80 or may be control to further increase the blowing volume of the airflow generation device 80 in operation.
  • control unit 37 controls the airflow. No need to control orientation.
  • the control unit 37 estimates the position of the person from the image data, and controls the louver 82 to generate an airflow toward the estimated position of the person. good too.
  • step S35 is performed for a certain period of time, and when the control ends, the process of step S31 is performed again.
  • the airflow control system 70 identifies changes in the person's clothing based on the image data.
  • a change in a person's clothing is an example of a feature related to a person's clothing.
  • the airflow control system 70 estimates the amount of dust generated from the person (whether or not dust is generated) based on the identified change in the person's clothing, and reduces the dust based on the estimated amount of dust generated. It controls the airflow generation device 40 that generates the airflow (airflow directed toward the person) for As a result, the airflow control system 70 can effectively reduce dust that is expected to be generated when a person puts on or takes off his/her clothes (keep it away from the person's surroundings).
  • step S31 the specifying unit 35 performs a process of specifying the texture of the clothing of the person in the image based on the image data.
  • a change in clothing may be determined by determining whether or not the texture of the clothing is different from the texture of the clothing.
  • a method based on the texture of the clothing can be considered in addition to the method based on the amount of clothing.
  • the airflow control system 70 can also estimate the amount of dust based on the texture of clothing or the amount of clothing. Therefore, the airflow control system 70 may comprehensively estimate the amount of dust based on two or more of the texture of clothing, the amount of clothing, and changes in clothing.
  • FIG. 12 is a flow chart of Operation Example 2 of the airflow control system 70 .
  • the specifying unit 35 of the control device 30 performs a process of specifying the movement of the human hair in the image based on the image data of the moving image (image data of a plurality of images) output by the camera 20 (S41).
  • the identification unit 35 may perform contour extraction processing of a person's head on each of image data of a plurality of images, and identify changes in the contour of the head over time as movement of the person's hair. can. Further, the identifying unit 35 may identify the movement of human hair using a machine learning model.
  • the estimating unit 36 determines whether or not the movement of the specified person's hair is large (S42). For example, the estimation unit 36 determines whether the amount of change in the shape or size of the contour is equal to or greater than a predetermined value. If the movement of the human hair is small, it is considered that the dust caused by the movement of the human hair is not generated. Therefore, when the estimation unit 36 determines that the movement of the human hair is small (No in S42), it estimates that no dust is generated (the amount of dust generation is zero) (S43). In this case, the processing of step S41 is continued periodically.
  • the control unit 37 performs control to strengthen the airflow generated by the airflow generation device 80 (S45). Control of the airflow generation device 80 is performed by transmitting a control signal from the communication unit 31 to the airflow generation device 80 . Specifically, the control unit 37 controls the amount of air blown by the blower fan 81 so that the airflow directed toward the person in the image becomes stronger.
  • the airflow control system 70 can effectively reduce the dust generated around the person due to the movement of the person's hair (keep it away from the person's surroundings).
  • the airflow control system 70 can guide generated dust to a predetermined area of the main space 60 .
  • the control to strengthen the airflow may be control to operate the stopped airflow generation device 80 or may be control to further increase the blowing volume of the airflow generation device 80 in operation.
  • control unit 37 controls the airflow. No need to control orientation.
  • the control unit 37 estimates the position of the person from the image data, and controls the louver 82 to generate an airflow toward the estimated position of the person. good too.
  • step S45 is performed for a certain period of time, and when the control ends, the process of step S41 is performed again.
  • the estimation unit 36 simply estimated the presence or absence of dust generation. However, the estimation unit 36 may determine the movement of human hair in multiple steps, and finely estimate the dust generation amount in multiple steps according to the determination results.
  • the airflow control system 70 identifies the movement of human hair based on the image data. Human hair movement is an example of a feature related to human hair.
  • the airflow control system 70 estimates the amount of dust generated from the person (whether or not dust is generated) based on the movement of the identified person's hair, and reduces the dust based on the estimated amount of dust generated. It controls an airflow generation device 80 that generates an airflow (airflow directed toward a person) for As a result, the airflow control system 70 can effectively reduce (remove from the surroundings of the person) dust expected to be generated based on the movement of the person's hair.
  • the airflow control system 70 can also estimate the amount of dust based on the person's hairstyle or the amount of hair of the person, similar to the airflow control system 10 . Therefore, the airflow control system 70 may comprehensively estimate the amount of dust based on two or more of the person's hairstyle, the amount of the person's hair, and the movement of the person's hair.
  • FIG. 13 is a flowchart of operation example 3 of the airflow control system 70 .
  • the specifying unit 35 of the control device 30 specifies the time-series data of the skeleton model of the person in the image based on the image data of the moving image (image data of a plurality of images) output by the camera 20 (S51).
  • a skeletal model is a model in which spheres indicating joint positions are connected by links.
  • FIG. 14 is a diagram showing an example of a skeleton model.
  • the time-series data of the skeletal model is, in other words, data indicating temporal changes in the coordinates of the joints. Any existing technique may be used to identify the skeleton model.
  • the estimating unit 36 determines whether or not the movement of the person in the image corresponds to a predetermined movement based on the identified time-series data of the skeleton model (S52). In other words, the estimator 36 detects a predetermined motion of the person appearing in the image.
  • the predetermined motion is at least one of motions expected to generate dust, such as motions of brushing off dust, motions of touching hair, and motions of putting on and taking off clothes.
  • the estimating unit 36 determines whether the movement of the person in the image is a predetermined one. It can be determined whether or not it corresponds to motion.
  • the predetermined movement may be detected by a method using pattern matching or a method using a machine learning model instead of the method using the skeleton model.
  • step S51 When the estimating unit 36 determines that the movement of the person reflected in the image does not correspond to the predetermined movement (No in S52), it estimates that no dust is generated (the amount of dust generated is zero) (S53). . In this case, the process of step S51 is continued periodically.
  • the control unit 37 performs control to strengthen the airflow generated by the airflow generation device 80 (S55).
  • Control of the airflow generation device 80 is performed by transmitting a control signal from the communication unit 31 to the airflow generation device 80 .
  • the control unit 37 controls the amount of air blown by the blower fan 81 so that the airflow directed toward the person in the image becomes stronger.
  • the airflow control system 70 can effectively reduce dust generated around the person due to the person's movement (keep dust away from the person's surroundings).
  • the airflow control system 70 can guide generated dust to a predetermined area of the main space 60 .
  • the control to strengthen the airflow may be control to operate the stopped airflow generation device 80 or may be control to further increase the blowing volume of the airflow generation device 80 in operation.
  • control unit 37 controls the airflow. No need to control orientation.
  • the control unit 37 estimates the position of the person from the image data, and controls the louver 82 to generate an airflow toward the estimated position of the person. good too.
  • step S55 is performed for a certain period of time, and when the control ends, the process of step S51 is performed again.
  • the estimation unit 36 simply estimated the presence or absence of dust generation. However, the estimation unit 36 may finely estimate the amount of generated dust depending on which of a plurality of predetermined movements the person's movement corresponds to.
  • the airflow control system 70 identifies human movement based on image data. Human movement is an example of features related to human movement.
  • the airflow control system 70 estimates the amount of dust generated from the person (whether or not dust is generated) based on the identified movement of the person, and reduces dust based on the estimated amount of dust generated. airflow (airflow directed toward a person) is controlled. As a result, the airflow control system 70 can effectively reduce dust expected to be generated based on the movement of the person (remove the dust from the surroundings of the person).
  • the airflow control system 10 may use the method of estimating the amount of generated dust described in the second embodiment. More specifically, the airflow control system 10 instructs a person standing in front of the airflow generation device 40 to perform a specific action, thereby strengthening the airflow from the airflow generation device 40 toward the person. can also be done. Similarly, the airflow control system 70 may use the method of estimating the dust generation amount described in the second embodiment.
  • each of the airflow control system 10 and the airflow control system 70 may estimate the dust generation amount by combining two or more of the features related to the person's clothing, the features related to the person's hair, and the features related to the person's movement. good.
  • Each of the airflow control system 10 and the airflow control system 70 determines the amount of dust generated based on the characteristics related to the person's clothing, the amount of dust generated based on the characteristics related to the person's hair, and the amount of dust generated based on the characteristics related to the movement of the person. The airflow may be controlled based on the total amount of dust generated.
  • each of the airflow generation device 40 and the airflow generation device 80 is a device that generates an airflow directed from the device to the person, but may be a device that generates an airflow directed from the person to the device. That is, each of the airflow generation device 40 and the airflow generation device 80 may include a suction fan instead of the blower fan.
  • the airflow control system 10 (or the airflow control system 70) includes the acquisition unit 34 that acquires image data of an image in which a person is captured, and based on the acquired image data, identifies features related to the appearance of the person. an estimating unit 36 for estimating the amount of dust generated by a person based on the identified features related to the appearance of the person; and a dust reducing unit 36 for reducing dust based on the estimated amount of dust generated. and a control unit 37 that controls the airflow generation device 40 (or the airflow generation device 80) that generates an airflow.
  • Such an airflow control system 10 can control the airflow based on the estimated dust generation amount.
  • features related to a person's appearance are features related to a person's clothing.
  • Such an airflow control system 10 can estimate the amount of dust generated from a person based on the characteristics of the person's clothing.
  • features related to a person's clothing include the texture of the person's clothing.
  • Such an airflow control system 10 can estimate the amount of dust generated by a person based on the texture of the person's clothing.
  • features related to a person's clothing include the amount of clothing worn by the person.
  • Such an airflow control system 10 can estimate the amount of dust generated by a person based on the amount of clothing worn by the person.
  • features related to a person's clothing include changes in the person's clothing.
  • Such an airflow control system 10 can estimate the amount of dust generated by a person based on changes in the person's clothing.
  • features related to human appearance are features related to human hair.
  • Such an airflow control system 10 is capable of estimating the amount of dust generated by a person based on the characteristics of the person's appearance.
  • features related to human hair include the amount of human hair.
  • Such an airflow control system 10 can estimate the amount of dust generated from a person based on the amount of human hair.
  • features related to human hair include a person's hairstyle.
  • Such an airflow control system 10 can estimate the amount of dust generated from a person based on the person's hairstyle.
  • features related to human hair include movement of human hair.
  • Such an airflow control system 10 can estimate the amount of dust generated from a person based on the movement of the person's hair.
  • features related to a person's appearance are features related to a person's movement.
  • Such an airflow control system 10 can estimate the amount of dust generated by a person based on the characteristics of the person's movement.
  • features related to human movement include the movement of a person dusting off.
  • Such an airflow control system 10 can estimate the amount of dust generated by a person based on the movement of the person sweeping dust.
  • the airflow control method executed by a computer such as the airflow control system 10 (or the airflow control system 70) includes an acquisition step of acquiring image data of an image in which a person appears, and based on the acquired image data, the appearance of the person. an estimating step of estimating the amount of dust generated from a person based on the identified features of appearance of a person; and reducing dust based on the estimated amount of dust generated. and a control step of controlling the airflow generation device 40 (or the airflow generation device 80) that generates an airflow for
  • Such an airflow control method can control the airflow based on the estimation result of the dust generation amount.
  • the airflow control system is implemented by a plurality of devices, but may be implemented as a single device.
  • the airflow control system may be implemented as a single device that corresponds to the controller.
  • each component included in the airflow control system may be distributed among the plurality of devices in any way.
  • processing executed by a specific processing unit may be executed by another processing unit.
  • order of multiple processes may be changed, and multiple processes may be executed in parallel.
  • each component may be realized by executing a software program suitable for each component.
  • Each component may be realized by reading and executing a software program recorded in a recording medium such as a hard disk or a semiconductor memory by a program execution unit such as a CPU or processor.
  • each component may be realized by hardware.
  • each component may be a circuit (or integrated circuit). These circuits may form one circuit as a whole, or may be separate circuits. These circuits may be general-purpose circuits or dedicated circuits.
  • general or specific aspects of the present invention may be implemented in a system, apparatus, method, integrated circuit, computer program, or recording medium such as a computer-readable CD-ROM.
  • any combination of systems, devices, methods, integrated circuits, computer programs and recording media may be implemented.
  • the present invention may be realized as a program for causing a computer to execute the airflow control method of the above embodiment, or as a computer-readable non-temporary recording medium storing such a program. may be implemented.

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Combustion & Propulsion (AREA)
  • Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Multimedia (AREA)
  • Fluid Mechanics (AREA)
  • Air Conditioning Control Device (AREA)
PCT/JP2022/024394 2021-06-29 2022-06-17 気流制御システム、及び、気流制御方法 WO2023276737A1 (ja)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2023531806A JP7702676B2 (ja) 2021-06-29 2022-06-17 エアシャワー用気流制御システム、及び、エアシャワー用気流制御方法

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2021108190 2021-06-29
JP2021-108190 2021-06-29

Publications (1)

Publication Number Publication Date
WO2023276737A1 true WO2023276737A1 (ja) 2023-01-05

Family

ID=84691690

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2022/024394 WO2023276737A1 (ja) 2021-06-29 2022-06-17 気流制御システム、及び、気流制御方法

Country Status (2)

Country Link
JP (1) JP7702676B2 (enrdf_load_stackoverflow)
WO (1) WO2023276737A1 (enrdf_load_stackoverflow)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS6365242A (ja) * 1986-09-08 1988-03-23 Sanki Eng Co Ltd 空気清浄システム
JP2000325700A (ja) * 1999-05-25 2000-11-28 Mitsubishi Electric Corp 無塵衣管理装置，エアシャワ室及びコンピュータ読み取り可能な記録媒体
JP2006348407A (ja) * 2005-06-15 2006-12-28 Trinc:Kk 無塵衣およびホコリ監視システム
JP2013167387A (ja) * 2012-02-15 2013-08-29 Hitachi Industrial Equipment Systems Co Ltd エアシャワー装置
JP2016109414A (ja) * 2014-11-28 2016-06-20 パナソニックＩｐマネジメント株式会社 除塵装置および除塵方法
JP2017026270A (ja) * 2015-07-27 2017-02-02 パナソニックＩｐマネジメント株式会社 集塵システムおよび集塵方法
JP2017096566A (ja) * 2015-11-25 2017-06-01 パナソニックＩｐマネジメント株式会社 集塵システムおよび集塵方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS5937319U (ja) * 1982-08-31 1984-03-09 羽藤　勝元 日除け
JP2021060136A (ja) * 2019-10-03 2021-04-15 清水建設株式会社 空気清浄システム
JP7348026B2 (ja) * 2019-10-24 2023-09-20 株式会社日立製作所 微粒子処理システム、微粒子処理制御装置及び微粒子処理制御プログラム

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS6365242A (ja) * 1986-09-08 1988-03-23 Sanki Eng Co Ltd 空気清浄システム
JP2000325700A (ja) * 1999-05-25 2000-11-28 Mitsubishi Electric Corp 無塵衣管理装置，エアシャワ室及びコンピュータ読み取り可能な記録媒体
JP2006348407A (ja) * 2005-06-15 2006-12-28 Trinc:Kk 無塵衣およびホコリ監視システム
JP2013167387A (ja) * 2012-02-15 2013-08-29 Hitachi Industrial Equipment Systems Co Ltd エアシャワー装置
JP2016109414A (ja) * 2014-11-28 2016-06-20 パナソニックＩｐマネジメント株式会社 除塵装置および除塵方法
JP2017026270A (ja) * 2015-07-27 2017-02-02 パナソニックＩｐマネジメント株式会社 集塵システムおよび集塵方法
JP2017096566A (ja) * 2015-11-25 2017-06-01 パナソニックＩｐマネジメント株式会社 集塵システムおよび集塵方法

Also Published As

Publication number Publication date
JP7702676B2 (ja) 2025-07-04
JPWO2023276737A1 (enrdf_load_stackoverflow) 2023-01-05

Similar Documents

Publication Publication Date Title
JP5876142B2 (ja) 存在センサ
CN105654512B (zh) 一种目标跟踪方法和装置
US10921806B2 (en) Moving robot
JP4968929B2 (ja) 画像処理装置及び画像処理方法
CN109945438B (zh) 空调器的控制方法、装置和空调器
JP7525990B2 (ja) 主被写体判定装置、撮像装置、主被写体判定方法、及びプログラム
JP7172394B2 (ja) 空気調和装置及び生体情報解析システム
JP2004513442A (ja) 外観及び幾何学的配置特質に基づき統計モデルを活用する画像処理システムでの人物の札付け
JP2011137589A (ja) 空気調和機、空気調和機の制御装置
CN102734193B (zh) 风扇控制系统、方法及具有该系统的风扇
JP6216596B2 (ja) 画像処理装置、撮像装置、空調システム、および、プログラム
JP2016048128A (ja) 空気調和機
JP2012037102A (ja) 人物識別装置、人物識別方法及び人物識別装置を備えた空気調和機
JP2005242759A (ja) 行動・意図推定システム、行動・意図推定方法、行動・意図推定プログラムおよびそのプログラムを記録したコンピュータ読み取り可能な記録媒体
TWI428508B (zh) 風扇控制系統、方法及具有該系統的風扇
WO2023276737A1 (ja) 気流制御システム、及び、気流制御方法
WO2022224833A1 (ja) 生活支援システム
JP4553384B2 (ja) 撮像装置及びその制御方法、コンピュータプログラム、記憶媒体
CN112683266B (zh) 机器人及其导航方法
JP7435621B2 (ja) 画像処理装置、および画像処理システム、並びに画像処理方法
JP6696687B2 (ja) 照明制御システム、照明制御方法、照明制御装置及びコンピュータプログラム
CN109812934B (zh) 送风系统、送风方法及计算机可读存储介质
CN112901542A (zh) 风扇控制方法、风扇及存储介质
WO2023276735A1 (ja) 気流制御システム、及び、気流制御方法
JP7311478B2 (ja) 情報処理装置、情報処理プログラム及び情報処理システム

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22832887

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2023531806

Country of ref document: JP

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22832887

Country of ref document: EP

Kind code of ref document: A1